no code implementations • 16 Jul 2022 • Antonio Barbalau, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, Jacob Dueholm, Bharathkumar Ramachandra, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #2 on Anomaly Detection on CUHK Avenue
no code implementations • 7 Jul 2022 • Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu
The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.
1 code implementation • 9 Dec 2021 • Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Barbalau, Radu Ionescu, Marius Popescu
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
1 code implementation • CVPR 2021 • Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Ranked #2 on Anomaly Detection on UCSD Peds2
Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +4
1 code implementation • NeurIPS 2020 • Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.
1 code implementation • 6 Jun 2020 • Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.